Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Martin Mächler: Catalogue data in Autumn Semester 2016

Name Prof. Dr. Martin Mächler
Address
Seminar für Statistik (SfS)
ETH Zürich, HG G 16
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 632 34 08
E-mailmaechler@stat.math.ethz.ch
URLhttp://stat.ethz.ch/~maechler
DepartmentMathematics
RelationshipAdjunct Professor

NumberTitleECTSHoursLecturers
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl
AbstractAbout 5 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 5 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
http://stat.ethz.ch/events/zukost
Course language is English or German and may depend on the speaker.
401-6221-00LNonparametric Regression Restricted registration - show details
Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.
1 credit1GM. Mächler
AbstractThis course focusses on nonparametric estimation of probability densities and regression functions. These recent methods allow modelling without restrictive assumptions such as 'linear function'. These smoothing methods require a weight function and a smoothing parameter. Focus is on one dimension, higher dimensions and samples of curves are treated briefly. Exercises at the computer.
ObjectiveKnowledge on estimation of probability densities and regression functions via various statistical methods.
Understanding of the choice of weight function and of the smoothing parameter, also done automatically.
Practical application on data sets at the computer.
401-6245-00LData Mining Information Restricted registration - show details
Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.
1 credit1GM. Mächler
AbstractBlock course only on prediction problems, aka "supervised learning".

Part 1, Classification: logistic regression, linear/quadratic discriminant analysis, Bayes classifier; additive and tree models; further flexible ("nonparametric") methods.

Part 2, Flexible Prediction: additive models, MARS, Y-Transformation models (ACE,AVAS); Projection Pursuit Regression (PPR), neural nets.
Objective
Content"Data Mining" is a large field from which in this block course, we only treat so called prediction problems, aka "supervised learning".

Part 1, Classification, recalls logistic regression and linear / quadratic discriminant analysis (LDA/QDA) and extends these (in the framework of 'Bayes classifier") to (generalized) additive (GAM) and tree models (CART), and further
mentions other flexible ("nonparametric") methods.

Part 2, Flexible Prediction (of continuous or "class" response/target) contains additive models, MARS, Y-Transformation models (ACE, AVAS); Projection Pursuit Regression (PPR), neural nets.
Lecture notesThe block course is based on (German language) lecture notes.
Prerequisites / NoticeThe exercises are done exlusively with the (free, open source) software "R"
(http://www.r-project.org). A final exam will also happen at the computers, using R (and your brains!).